In this work, an integrated contactless multiple hand feature acquisition system is designed. The system can capture
palmprint, palm vein, and palm dorsal vein images simultaneously. Moreover, the images are captured in a contactless
manner, that is, users need not to touch any part of the device when capturing. Palmprint is imaged under visible
illumination while palm vein and palm dorsal vein are imaged under near infrared (NIR) illumination. The capturing is
controlled by computer and the whole process is less than 1 second, which is sufficient for online biometric systems.
Based on this device, this paper also implements a contactless hand-based multimodal biometric system. Palmprint, palm
vein, palm dorsal vein, finger vein, and hand geometry features are extracted from the captured images. After similarity
measure, the matching scores are fused using weighted sum fusion rule. Experimental results show that although the
verification accuracy of each uni-modality is not as high as that of state-of-the-art, the fusion result is superior to most of
the existing hand-based biometric systems. This result indicates that the proposed device is competent in the application
of contactless multimodal hand-based biometrics.
It has been demonstrated that multibiometrics can produce higher accuracy than single biometrics. This is mainly because the use of multiple biometric traits of the subject enables more information to be used for identification or verification. In this paper, we focus on bimodal biometrics and propose a novel representation and recognition approach to bimodal biometrics. This approach first denotes the biometric trait sample by a complex vector. Then, it represents the test sample through the training samples and classifies the test sample as follows: let the test sample be expressed as a linear combination of all the training samples each being a complex vector. The proposed approach obtains the solution by solving a linear system. After evaluating the effect, in representing the test sample of each class, the approach classifies the test sample into the class that makes the greatest effect. The approach proposed is not only novel but also simple and computationally efficient. A large number of experiments show that our method can obtain promising results.
Multibiometrics can obtain a higher accuracy than the single biometrics by simultaneously using multiple biometric traits of the subject. We note that biometric traits are usually in the form of images. Thus, how to properly fuse the information of multiple biometric images of the subject for authentication is crucial for multibiometrics. We propose a novel image-based linear discriminant analysis (IBLDA) approach to fuse two biometric traits (i.e., bimodal biometric images) of the same subject in the form of matrix at the feature level. IBLDA first integrates two biometric traits of one subject into a complex matrix and then directly extracts low-dimensional features for the integrated biometric traits. IBLDA also enables more information to be exploited than the matching score level fusion and the decision level fusion. Compared to linear discriminant analysis (LDA), IBLDA has the following advantages: First, it can overcome the small sample size problem that conventional LDA usually suffers from. Second, IBLDA solves the eigenequation at a low computational cost. Third, when storing the scatter matrices IBLDA will not bring as heavy a memory burden as conventional LDA. We also clearly show the theoretical foundation of the proposed method. The experiment result shows that the proposed method can obtain a high classification accuracy.
A palmprint identification system recognizes a query palmprint image by searching for its nearest neighbor from among all the templates in a database. When applied on a large-scale identification system, it is often necessary to speed up the nearest-neighbor searching process. We use competitive code, which has very fast feature extraction and matching speed, for palmprint identification. To speed up the identification process, we extend the cover tree method and propose to use a set of cover trees to facilitate the fast and accurate nearest-neighbor searching. We can use the cover tree method because, as we show, the angular distance used in competitive code can be decomposed into a set of metrics. Using the Hong Kong PolyU palmprint database (version 2) and a large-scale palmprint database, our experimental results show that the proposed method searches for nearest neighbors faster than brute force searching.
This paper presents a new personal authentication system that simultaneously exploits 2D and 3D palmprint features.
Here, we aim to improve the accuracy and robustness of existing palmprint authentication systems using 3D palmprint
features. The proposed system uses an active stereo technique, structured light, to capture 3D image or range data of the
palm and a registered intensity image simultaneously. The surface curvature based method is employed to extract
features from 3D palmprint and Gabor feature based competitive coding scheme is used for 2D representation. We
individually analyze these representations and attempt to combine them with score level fusion technique. Our
experiments on a database of 108 subjects achieve significant improvement in performance (Equal Error Rate) with the
integration of 3D features as compared to the case when 2D palmprint features alone are employed.
This paper investigates a new approach for human ear identification using holistic
grey-level information. We employ Log-Gabor wavelets to extract the phase
information, i.e. ear-codes, from the 1D gray-level signals. Thus each ear is
represented by a unique ear code or (phase template). The query ear images are
compared with those in the database using Hamming distance. The minimum
Hamming distance obtained from the rotation of ear template is used to authenticate
the user. Our experiments on two different public ear databases achieve promising
results and suggest its utility in ear-based authentication. This paper also illustrates
that the phase information extracted from ear images can achieve significant
performance improvement as compared to appearance-based approach employed in
the literature.
A palmprint can be represented using different features and the different representations reflect the different characteristic of a palmprint. Fusion of multiple palmprint features may enhance the performance of a palmprint authentication system. This paper investigates the fusion of two types of palmprint information: the phase (called PalmCode) and the orientation (called OrientationCode). The PalmCode is extracted using the 2-D Gabor filters based algorithm and the OrientationCode is computed using several directional templates. Then several fusion strategies are investigated and compared. The experimental results show that the fusion of the PalmCode and OrientationCode using the Product, Sum and Weighted Sum strategies can greatly improve the accuracy of palmprint authentication, which is up to 99.6%.
This paper investigates the performance improvement for palmprint authentication using multiple classifiers. The proposed methods on personal authentication using palmprints can be divided into three categories; appearance- , line -, and texture-based. A combination of these approaches can be used to achieve higher performance. We propose to simultaneously extract palmprint features from PCA, Line detectors and Gabor-filters and combine their corresponding matching scores. This paper also investigates the comparative performance of simple combination rules and the hybrid fusion strategy to achieve performance improvement. Our experimental results on the database of 100 users demonstrate the usefulness of such approach over those based on individual classifiers.
This article presents a new type of 3D color digital imaging system. First, we briefly introduce the current 3D imaging technologies: passive and active sensing. Second, a new type of 3D imaging system is introduced. Using this system, the shape of an object can be digitized and the texture of this object surface can also be gained simultaneously. The emphasis of the paper is put on the description of hardware design and software framework design of the system. The paper also presents the 3D digital image made on a human face as an experimental result. Then, the potential industrial applications such as reverse engineering, digitization of museum artifacts, inspection, biomedical imaging, home shopping, film-making and virtual reality, etc., are described. Finally, we make conclusion and future development on 3D imaging.
Paimprint is a new biometric method to recognize a person. The most important feature of paimprint is the lines. In this paper, a set of line detector is devised for paimprint. There are two parameters in these detectors, one controls the smoothness and connection of the lines, the other controls the width of lines which can be detected. The lines in different directions are detected by corresponding direction detectors and then fused into one edge image. In training stage, the lines of the training samples are represented and stored with chain code. In the verification stage, the lines are matched using Hausdorif distance. Experimental results show the efficiency of this method.
The wavelet theory has become hot in the last few years for its important relative characters, such as, subband coding, multiresolution analysis and filter banks. In this paper, we propose a novel method of feature extraction for palmprint identification based on wavelet transform, which is very efficient to handle the textural characteristics of palmprint images at low resolution. The matching results show that the proposed feature extraction method is efficient in terms of matching accuracy and computational speed.
This paper presents a new approach to palmprint retrieval for personal identification. Three key issues in image retrieval are considered - feature selection, similarity measures and dynamic search for the best matching of the sample in the image database. We propose a texture-based method for palmprint feature representation. The concept of texture energy is introduced to define a palm print's global and local features, which are characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination. The search is carried out in a layered fashion: first global features are used to guide the fast selection of a small set of similar candidates from the database from the database and then local features are used to decide the final output within the candidate set. The experimental results demonstrate the effectiveness and accuracy of the proposed method.
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